labelGo

<p>Guide Language:<a href="https://github.com/cnyvfang/labelGo-Yolov5AutoLabelImg/blob/master/readme_zh_cn.md">简体中文</a></p> <p>A graphical Semi-automatic annotation tool based on <a href="https://github.com/tzutalin/labelImg">labelImg</a> and <a href="https://github.com/ultralytics/yolov5">YOLOv5</a></p> <p>Semi-automatic annotation of datasets by existing yolov5 pytorch models</p>

News

labelGo now supports the latest version of YOLOv5, and automatic classes.txt file generation

Demonstration of semi-automatic labeling function

Demonstration of converting Yolo format to VOC format with one click

Note

<p>If there is a problem, please put it forward in the issue.</p> <p>The annotation file is saved in the same location as the picture folder.</p> <p>Recommended version of python: python 3.8.</p> <p>Recommended for conda environments.</p> <p>The item is completely free and it is forbidden to sell the item in any way. </p> <p>This project has support for the latest version of YOLOv5, if you need to use an older version that supports YOLOv5 version5, you can find the source code in Release. </p>

Installation and use

<p>1.Fetching projects from git</p>

git clone https://github.com/cnyvfang/labelGo-Yolov5AutoLabelImg.git

<p>2.Switching the operating directory to the project directory</p>

cd labelGo-Yolov5AutoLabelImg

<p>3.Installation environment</p>

pip install -r requirements.txt

<p>4.Launching applications</p>

python labelGo.py

<p>5. Click on the "Open directory" button to select the folder where the images are stored</p> <p>6. Click on the "Auto Annotate" button to confirm that the information is correct and then select the trained yolov5 pytorch model to complete the auto annotation</p> <p>7. Adjust the automatic annotation results according to the actual requirements and save them</p>

Acknowledgements

Thanks to tangtang666 for submitting support for the latest version of YOLOv5

Thanks to Iceprism for fixing the bugs in the Chinese version.